Method for constructing prediction model of auto trips quantity and prediction method and system

A method for constructing a prediction model of an auto trips quantity and a prediction method and system are disclosed. The prediction model construction method designs a deep neural network Multitask GCN-LSTM based on GCN and LSTM for predicting the auto trips quantity. The deep neural network com...

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Bibliographic Details
Main Authors Duan, Zongtao, Kang, Jun, Fan, Na, Zhao, Bin, Wang, Yuehan, Chen, Zhe
Format Patent
LanguageEnglish
Published 28.05.2024
Subjects
PHYSICS
SIGNALLING
TRAFFIC CONTROL SYSTEMS
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Summary:A method for constructing a prediction model of an auto trips quantity and a prediction method and system are disclosed. The prediction model construction method designs a deep neural network Multitask GCN-LSTM based on GCN and LSTM for predicting the auto trips quantity. The deep neural network comprises three modules, wherein the three modules are respectively used for extracting a spatial correlation, a temporal correlation and a feature fusion. The prediction method and system predict the auto trips quantity based on a model constructed. By considering a road segment local relationship and a road segment global relationship and taking an auto arrival quantity as a related task in constructing the model, the prediction model construction method uses a multi-task learning method to avoid overfitting of the deep neural network and reduce a prediction error of the auto trips quantity effectively.
Bibliography:Application Number: US202017433242